Open Access<p>Added value of histogram analysis of apparent diffusion coefficient maps for differentiating triple-negative breast cancer from other subtypes of breast cancer on standard MRI</p>
Author(s) -
Hongli Liu,
MinHua Zong,
Wei Han,
Cong Wang,
Jianjuan Lou,
Siqi Wang,
Qi-Gui Zou,
Yanni Jiang
Publication year - 2019
Publication title -
cancer management and research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.024
H-Index - 40
ISSN - 1179-1322
DOI - 10.2147/cmar.s210583
Subject(s) - effective diffusion coefficient , medicine , breast cancer , receiver operating characteristic , triple negative breast cancer , magnetic resonance imaging , breast mri , diffusion mri , nuclear medicine , univariate analysis , margin (machine learning) , area under the curve , cancer , radiology , multivariate analysis , mammography , machine learning , computer science
Triple-negative breast cancers generally occur in young women with remarkable potential to be aggressive. It will be of great help to detect this subtype of tumor early. To retrospectively evaluate the performance of histogram analysis of apparent diffusion coefficient (ADC) maps in distinguishing triple-negative breast cancer (TNBC) from other subtypes of breast cancer (non-TNBC), when combined with magnetic resonance imaging (MRI) features.